This is a experimental python project that extracts IMDB reviews for a movie, classifies them and generate a result html file
The project is based on the programming assignments and what I've learned in **Udacity CSC 101 class** and **Stanford NLP class**
The script is also used as a plugin in my term project for my Distributed System class this semster
Usage:
To Train:
python imdb.py -t LIST_FILE MAX_COMMENT_COUNT
To Classify:
python imdb.py -c OUTPUT_HTML_PATH MOVIE_TITLE [MAX_COMMENT_COUNT]
The files in the lists directory is the movie lists I used to train the NaiveBayes classifier, they come from random titles in
* IMDB Top 250 (http://www.imdb.com/chart/top)
* IMDB Bottom 100 (http://www.imdb.com/chart/bottom)
* The Best 1,000 Movies Ever Made (http://www.nytimes.com/ref/movies/1000best.html)
To simplify the problem, the train process will flag reviews with more than 6 stars as a positive review, reviews with less than 4 stars as a negative review
Trained data is stored in trained.raw as a plain text file
* Future Works:
Try other algorithms to improve the accuracy
Make a online version